Comparative Analysis of LMS, NLMS, and RLS Adaptive Filters in Vehicle Automation Systems under Mixed Noise Conditions

Ibrahim  beram jasim; Roaya S. Abdalrahman; Mays kifah faeq

doi:10.56286/aj0zzx91

Authors

Ibrahim beram jasim Computer science department, Computer science and information technology college, University of kirkuk -Iraq https://orcid.org/0000-0001-8618-2076
Roaya S. Abdalrahman Electronic and Control Department, Technical Engineering College Kirkuk, Northern Technical University-Iraq https://orcid.org/0009-0005-4812-5069
Mays kifah faeq Computer science department, Computer science and information technology college, University of kirkuk -Iraq https://orcid.org/0009-0003-6455-3480

DOI:

https://doi.org/10.56286/aj0zzx91

Keywords:

Adaptive noise cancellation, Least Mean Squares (LMS), Normalized Least Mean Squares (NLMS), Recursive Least Squares (RLS), mixed noise, vehicular automation systems.

Abstract

In modern vehicle automatic systems, noise interference presents a significant obstacle to the precision and dependability of sensor-based control and communication. This study offers a comparative performance evaluation of three adaptive filtering algorithms—Least Mean Squares (LMS), Normalized LMS (NLMS), and Recursive Least Squares (RLS)—utilized for adaptive noise cancellation (ANC) under mixed noise conditions. A MATLAB-based graphical user interface (GUI) simulation was created to estimate and illustrate the performance of each method across three noise types: Gaussian, Impulsive and Mixed. The results informed that RLS attained the greatest signal to noise ratio (SNR) enhancement and with the minimal mean square error (MSE), where as the NLMS offered a proficient equilibrium between velocity and computing complexity. This research evaluation that appropriateness of NLMS in real time vehicular control applications and RLS requiring high accuracy .